"Conda-Forge" matching MCP servers:

Enables AI agents to fully control the Forge Engine (Godot fork) by manipulating scene trees, modifying node properties, and managing game scripts. It allows agents to perform development tasks such as running projects, capturing screenshots, and executing GDScript through a WebSocket connection.

Allows LLMs to execute Python code in a specified Conda environment, enabling access to necessary libraries and dependencies for efficient code execution.

Provides up-to-date Python package manager commands by cross-referencing official pip, poetry, uv, and conda documentation with automatic weekly updates.

A tool that generates MCP servers for Smithery with Cursor IDE integration, allowing users to quickly create servers from templates such as Basic, Web Search, or Database interactions.

Enables AI agents to investigate and repair Python/pytest repositories in isolated Git worktrees with audit trails, without modifying the original repository.

Enables MCP-capable agents to author, compile, run, and commit generative music E-- Recipes from a library catalog, completing the full authoring loop.

It lets any MCP-capable AI agent operate a real Siemens TIA Portal project — configuring hardware and networks, creating blocks, writing SCL, compiling, simulating via PLCSIM Advanced, and reading results back for verification. It runs locally alongside TIA Portal through Openness, so agents work with live projects rather than code snippets.

Turns any OpenAPI spec into a single-tool MCP server where the model writes JavaScript against a typed api object in a QuickJS sandbox, so multi-step tasks run in one round trip and only the final result reaches context. Credentials, rate limiting, retries, write gating and audit logging stay on the host, outside the sandbox.

TACIT (Tracked Agent Capabilities In Types) is a safety harness for AI agents. Instead of calling tools directly, agents write code in Scala 3 with capture checking: a type system that statically tracks capabilities and enforces that agent code cannot forge access rights, cannot perform effects beyond its budget, and cannot leak information from pure sub-computations. It provides an MCP interface,

Enables LLMs to run Python code and scripts across system and conda environments, list and install packages, and read or write text and binary files within an isolated working directory. It also provides prompt templates for generating, refactoring, and debugging Python code.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes prompts for generating, refactoring, and debugging Python code.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes environment, package, and file resources plus prompt templates for creating, refactoring, and debugging Python code.